Objective:To explore the independent factors influencing extraprostatic extension (EPE) after radical prostatectomy(RP) in patients with clinically localized prostate cancer by utilizing the Surveillance, Epidemiology, and End Results (SEER) database. A nomogram model was developed and externally validated.Methods:Clinical and pathological data of 20 916 clinically localized prostate cancer patients (T 1-2N 0M 0) who underwent RP between 2010 and 2021 were extracted from the SEER database. The mean age was (61.71±7.09) years old, and a total of 17 835 patients (85.3%) were married.There were 2 243 patients (10.7%) with prostate-specific antigen (PSA) <4 ng/ml, 14 831 patients (70.9%) with ≥4 and <10 ng/ml, and 2 965 patients (14.2%) with ≥10 and <20 ng/ml. There were 14 870 patients (71.1%) with clinical staging of stage T 1, and 6 046 patients (28.9%) with T 2. There were 48 patients (0.2%) with pathological staging of stage T 1, 15 794 (75.5%) with T 2, 5 001(23.9%) with T 3, and 73 (0.3%) with T 4 stage after radical surgery.The patients of SEER database were divided into training and internal validation groups in a 7∶3 ratio by using stratified sampling. Additionally, data were collected for 75 clinically localized prostate cancer patients who underwent RP at the Second Affiliated Hospital of Zhengzhou University from September 2019 to September 2024, serving as the external validation group.The mean age was(65.39±7.45) years old. Among them, 73 (97.3%) were married. There were 2 patients (2.7%) with PSA <4 ng/ml, 17 patients (22.7%) with ≥4 and <10 ng/ml, and 34 patients (45.3%) with ≥10 and <20 ng/ml. There were 47 patients (62.7%) with clinical staging of stage T 1, and 28 patients (37.3%) with T 2. There were 7 patients (9.3%) with pathological staging of stage T 1, 48 patients (64.0%)with T 2, 18 patients (24.0%) with T 3, and 2 patients (2.7%) with T 4 stage after radical surgery. All patients were categorized into organ-confined (OC) and EPE groups based on post-surgical pathology. Univariate and multivariate logistic regression analyses, with a stepwise backward selection, were performed on the training group to identify independent risk factors of EPE, which were used to construct a nomogram model. Model performance was assessed using receiver operating characteristic (ROC) curve area under the curve (AUC), calibration curves, and decision curve analysis (DCA) for the training group, internal validation group, and external validation group. Results:EPE was observed in 3 585 cases (24.5%), 1 489 cases (23.8%), and 20 cases (26.7%) in the training, internal validation, and external validation groups, respectively. Logistic regression analyses identified preoperative age ( OR=1.026, P<0.001), PSA levels (≥10 and <20 ng/ml: OR=1.790, P<0.001; ≥20 ng/ml: OR=2.683, P<0.001), tumor maximum diameter (10-20 mm: OR=2.051, P<0.001; >20 mm: OR=3.937, P<0.001), biopsy Gleason score (score 7: OR=1.911, P<0.001; score 8: OR=2.906, P<0.001; score 9: OR = 5.278, P<0.001; score 10: OR=4.421, P=0.003), number of positive biopsy cores (≥4 cores: OR=1.260, P<0.001), and their proportion of total cores ( OR=1.012, P<0.001) as independent predictors of EPE. The nomogram model demonstrated good predictive performance, with AUC of 0.741, 0.748, and 0.724 in the training, internal validation, and external validation groups, respectively. Calibration and DCA curves confirmed the model’s excellent stability and generalizability. Conclusions:Age, PSA levels, maximum tumor diameter, biopsy Gleason score, number of positive biopsy cores, and their proportion of total cores are independent predictors of EPE after RP in clinically localized prostate cancer. The constructed model effectively predicts the risk of EPE occurrence.

Objective:To explore the independent factors influencing extraprostatic extension (EPE) after radical prostatectomy(RP) in patients with clinically localized prostate cancer by utilizing the Surveillance, Epidemiology, and End Results (SEER) database. A nomogram model was developed and externally validated.Methods:Clinical and pathological data of 20 916 clinically localized prostate cancer patients (T 1-2N 0M 0) who underwent RP between 2010 and 2021 were extracted from the SEER database. The mean age was (61.71±7.09) years old, and a total of 17 835 patients (85.3%) were married.There were 2 243 patients (10.7%) with prostate-specific antigen (PSA) <4 ng/ml, 14 831 patients (70.9%) with ≥4 and <10 ng/ml, and 2 965 patients (14.2%) with ≥10 and <20 ng/ml. There were 14 870 patients (71.1%) with clinical staging of stage T 1, and 6 046 patients (28.9%) with T 2. There were 48 patients (0.2%) with pathological staging of stage T 1, 15 794 (75.5%) with T 2, 5 001(23.9%) with T 3, and 73 (0.3%) with T 4 stage after radical surgery.The patients of SEER database were divided into training and internal validation groups in a 7∶3 ratio by using stratified sampling. Additionally, data were collected for 75 clinically localized prostate cancer patients who underwent RP at the Second Affiliated Hospital of Zhengzhou University from September 2019 to September 2024, serving as the external validation group.The mean age was(65.39±7.45) years old. Among them, 73 (97.3%) were married. There were 2 patients (2.7%) with PSA <4 ng/ml, 17 patients (22.7%) with ≥4 and <10 ng/ml, and 34 patients (45.3%) with ≥10 and <20 ng/ml. There were 47 patients (62.7%) with clinical staging of stage T 1, and 28 patients (37.3%) with T 2. There were 7 patients (9.3%) with pathological staging of stage T 1, 48 patients (64.0%)with T 2, 18 patients (24.0%) with T 3, and 2 patients (2.7%) with T 4 stage after radical surgery. All patients were categorized into organ-confined (OC) and EPE groups based on post-surgical pathology. Univariate and multivariate logistic regression analyses, with a stepwise backward selection, were performed on the training group to identify independent risk factors of EPE, which were used to construct a nomogram model. Model performance was assessed using receiver operating characteristic (ROC) curve area under the curve (AUC), calibration curves, and decision curve analysis (DCA) for the training group, internal validation group, and external validation group. Results:EPE was observed in 3 585 cases (24.5%), 1 489 cases (23.8%), and 20 cases (26.7%) in the training, internal validation, and external validation groups, respectively. Logistic regression analyses identified preoperative age ( OR=1.026, P<0.001), PSA levels (≥10 and <20 ng/ml: OR=1.790, P<0.001; ≥20 ng/ml: OR=2.683, P<0.001), tumor maximum diameter (10-20 mm: OR=2.051, P<0.001; >20 mm: OR=3.937, P<0.001), biopsy Gleason score (score 7: OR=1.911, P<0.001; score 8: OR=2.906, P<0.001; score 9: OR = 5.278, P<0.001; score 10: OR=4.421, P=0.003), number of positive biopsy cores (≥4 cores: OR=1.260, P<0.001), and their proportion of total cores ( OR=1.012, P<0.001) as independent predictors of EPE. The nomogram model demonstrated good predictive performance, with AUC of 0.741, 0.748, and 0.724 in the training, internal validation, and external validation groups, respectively. Calibration and DCA curves confirmed the model’s excellent stability and generalizability. Conclusions:Age, PSA levels, maximum tumor diameter, biopsy Gleason score, number of positive biopsy cores, and their proportion of total cores are independent predictors of EPE after RP in clinically localized prostate cancer. The constructed model effectively predicts the risk of EPE occurrence.

Subgaleal hematomas are accumulation of blood between periosteum and galea aponeurosis. A 2-year-old male Chihuahua was presented with a severe head swelling after trauma. Radiography and computed tomography (CT) showed a massive swelling encircling the entire calvarial vault, extending toward the cervical neck and crossing the suture line. It was heterogeneously, mild hyperdense fluid to soft tissue attenuating with contrast enhancement on CT images. On day 4, physical and imaging examination showed resolution of the calvarial swelling. Subgaleal hematoma should be considered as a differential diagnosis when there is a massive soft tissue swelling over the skull on physical and imaging examinations.

Objective:To investigate the application value of combining Prostate Imaging Reporting and Data System (PI-RADS v2.1) score and Systemic Immune-Inflammation Index (SII) in predicting pathological upgrading in patients with localized prostate cancer after radical prostatectomy(RP).Methods:A retrospective analysis was conducted on clinical data from 76 patients with localized prostate cancer who underwent prostate biopsy and radical prostatectomy at the Second Affiliated Hospital of Zhengzhou University between September 2019 and May 2024. The median age was 68 (65, 71) years. Total prostate-specific antigen (tPSA) was 17.4 (8.4, 30.9) ng/ml, and prostate volume was 43.1 (29.9, 58.9) ml. PI-RADS scores were ≤3 in 22 cases (28.9%) and >3 in 54 cases (71.1%). According to the International Society of Urological Pathology (ISUP) grading of biopsy specimens, 31 patients (40.8%) were classified as Group <3 and 45 patients (59.2%) as Group ≥3. Postoperatively, 25 patients (32.9%) were classified as ISUP Group <3, and 51 patients (67.1%) as Group ≥3. Pathological upgrading was defined as either: ①a higher ISUP grade in postoperative specimens compared to biopsy specimens or; ②benign prostate tissue identified in biopsy specimens but confirmed as prostate cancer postoperatively. Clinical data were compared between the pathological upgrade and non-upgrade groups. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for pathological upgrading and to construct a nomogram model. Receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of individual indicators (PI-RADS, SII, %PSA, and the proportion of tumor tissue in biopsy specimens) and the combined nomogram model. Internal validation was conducted using cross-validation, and calibration and decision curves were generated to assess the nomogram′s accuracy and clinical net benefit.Results:Among the 76 patients included, 10 (13.2%) experienced pathological downgrading, 36 (47.4%) had consistent grading, and 30 (39.5%) experienced pathological upgrading. The platelet-to-lymphocyte ratio (PLR) [118.2(93.5, 139.1) vs. 95.2(79.3, 116.4), P=0.021], SII [394.8(331.0, 513.6) vs. 338.8(217.2, 407.8), P=0.002], and the number of cases with a PI-RADS score >3 [26 cases(86.7%) vs. 28 cases(60.9%), P=0.015] were significantly higher in the pathological upgrade group than in the non-upgrade group. Conversely, the percentage of positive biopsy cores [35.9%(12.6%, 51.8%) vs. 43.8%(21.0%, 92.1%), P=0.045], the proportion of tumor tissue in biopsy specimens [6.9%(1.3%, 20.1%) vs. 19.3%(9.1%, 58.4%), P<0.01], and the number of cases in ISUP biopsy Group ≥3 [12 cases (40.0%) vs. 33 cases (71.7%), P=0.006] were significantly lower in the upgrade group (all P < 0.05). Univariate and multivariate logistic regression analyses showed that PI-RADS score( OR=17.111, 95% CI 2.388-122.592, P<0.01), SII( OR=1.009, 95% CI 1.001-1.016, P=0.028), %PSA ( OR=0.003, 95% CI 0.002-0.004, P<0.01), and the proportion of tumor tissue in biopsy specimens ( OR=0.899, 95% CI 0.837-0.966, P<0.01) were independent predictors of pathological upgrading. The area under the ROC curve (AUC) for PI-RADS, SII, %PSA, and the proportion of tumor tissue in biopsy specimens were 0.607, 0.711, 0.618, and 0.778, respectively. The combined AUC for %PSA and the proportion of tumor tissue was 0.791, while the combined AUC of the four-indicator nomogram model was 0.914. The DeLong test indicated a statistically significant difference in diagnostic performance between the two models ( P<0.01). Calibration and decision curves demonstrated good accuracy and clinical net benefit for the nomogram model. Conclusions:The PI-RADS v2.1 score and SII have significant predictive value for pathological upgrading after radical prostatectomy in prostate cancer. A nomogram model combining PI-RADS, SII, %PSA, and the proportion of tumor tissue in biopsy specimens shows excellent predictive performance.

Objective To investigate the synergistic effect of Shugan Huatan Sanjie prescription(SHSF)and paclitaxel on MCF-7/PTX cells of breast cancer and its mechanism.Methods The PTX-resistant breast cancer cell line MCF-7/PTX was established by continuous induction of low concentration.The effects of different concentrations of PTX on MCF-7 and MCF-7/PTX cells.Then the proliferation of MCF-7/PTX cells by SHSF containing serum were detected by MTT assay,and the drug resistance index(RI)and reversion times were calculated according to the IC50 value.MCF-7/PTX cells were divided into blank control group,PTX group(45 nmol·L-1 PTX),SHSF group(7.5％SHSF drug-containing serum)and PTX+SHSF group(45 nmol·L-1 PTX+7.5％SHSF drug-containing serum).The cells were treated with drugs for 24 h.The apoptosis level of each group was detected by flow cytometry.The expression levels of apoptotic proteins Bax,Bcl-2,p-Akt(ser473),Akt,p-mTOR(Ser2448)and mTOR were detected by Western blot.Results The PTX-resistant cell line MCF-7/PTX was established successfully,and the RI value was 6.70.The proliferative activity of MCF-7/PTX cells decreased in a concentration-dependent manner with the increase of SHSF drug-containing serum concentration.And the resistance reversal ratio of 7.5％SHSF serum to MCF-7/PTX cells was 3.48.Compared with blank control group,the apoptosis levels of MCF-7/PTX cells in PTX group and SHSF group were significantly increased(P＜0.01),the protein expression level of Bax was significantly up-regulated(P＜0.01),and the protein expression levels of Bcl-2,p-Akt/Akt and p-mTOR/mTOR were significantly down-regulated(P＜0.05);Compared with PTX group,the apoptosis level in PTX+SHSF group was significantly increased(P＜0.01),the expression level of Bax protein was significantly up-regulated(P＜0.01),and the expression levels of Bcl-2,p-Akt and p-mTOR protein were significantly down-regulated(P＜0.01).Conclusion Shugan Huatan Sanjie Recipe promotes apoptosis by inhibiting Akt/mTOR pathway,and thus plays a synergistic effect with paclitaxel on MCF-7/PTX cells of breast cancer.

Objective:To investigate the application value of combining Prostate Imaging Reporting and Data System (PI-RADS v2.1) score and Systemic Immune-Inflammation Index (SII) in predicting pathological upgrading in patients with localized prostate cancer after radical prostatectomy(RP).Methods:A retrospective analysis was conducted on clinical data from 76 patients with localized prostate cancer who underwent prostate biopsy and radical prostatectomy at the Second Affiliated Hospital of Zhengzhou University between September 2019 and May 2024. The median age was 68 (65, 71) years. Total prostate-specific antigen (tPSA) was 17.4 (8.4, 30.9) ng/ml, and prostate volume was 43.1 (29.9, 58.9) ml. PI-RADS scores were ≤3 in 22 cases (28.9%) and >3 in 54 cases (71.1%). According to the International Society of Urological Pathology (ISUP) grading of biopsy specimens, 31 patients (40.8%) were classified as Group <3 and 45 patients (59.2%) as Group ≥3. Postoperatively, 25 patients (32.9%) were classified as ISUP Group <3, and 51 patients (67.1%) as Group ≥3. Pathological upgrading was defined as either: ①a higher ISUP grade in postoperative specimens compared to biopsy specimens or; ②benign prostate tissue identified in biopsy specimens but confirmed as prostate cancer postoperatively. Clinical data were compared between the pathological upgrade and non-upgrade groups. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for pathological upgrading and to construct a nomogram model. Receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of individual indicators (PI-RADS, SII, %PSA, and the proportion of tumor tissue in biopsy specimens) and the combined nomogram model. Internal validation was conducted using cross-validation, and calibration and decision curves were generated to assess the nomogram′s accuracy and clinical net benefit.Results:Among the 76 patients included, 10 (13.2%) experienced pathological downgrading, 36 (47.4%) had consistent grading, and 30 (39.5%) experienced pathological upgrading. The platelet-to-lymphocyte ratio (PLR) [118.2(93.5, 139.1) vs. 95.2(79.3, 116.4), P=0.021], SII [394.8(331.0, 513.6) vs. 338.8(217.2, 407.8), P=0.002], and the number of cases with a PI-RADS score >3 [26 cases(86.7%) vs. 28 cases(60.9%), P=0.015] were significantly higher in the pathological upgrade group than in the non-upgrade group. Conversely, the percentage of positive biopsy cores [35.9%(12.6%, 51.8%) vs. 43.8%(21.0%, 92.1%), P=0.045], the proportion of tumor tissue in biopsy specimens [6.9%(1.3%, 20.1%) vs. 19.3%(9.1%, 58.4%), P<0.01], and the number of cases in ISUP biopsy Group ≥3 [12 cases (40.0%) vs. 33 cases (71.7%), P=0.006] were significantly lower in the upgrade group (all P < 0.05). Univariate and multivariate logistic regression analyses showed that PI-RADS score( OR=17.111, 95% CI 2.388-122.592, P<0.01), SII( OR=1.009, 95% CI 1.001-1.016, P=0.028), %PSA ( OR=0.003, 95% CI 0.002-0.004, P<0.01), and the proportion of tumor tissue in biopsy specimens ( OR=0.899, 95% CI 0.837-0.966, P<0.01) were independent predictors of pathological upgrading. The area under the ROC curve (AUC) for PI-RADS, SII, %PSA, and the proportion of tumor tissue in biopsy specimens were 0.607, 0.711, 0.618, and 0.778, respectively. The combined AUC for %PSA and the proportion of tumor tissue was 0.791, while the combined AUC of the four-indicator nomogram model was 0.914. The DeLong test indicated a statistically significant difference in diagnostic performance between the two models ( P<0.01). Calibration and decision curves demonstrated good accuracy and clinical net benefit for the nomogram model. Conclusions:The PI-RADS v2.1 score and SII have significant predictive value for pathological upgrading after radical prostatectomy in prostate cancer. A nomogram model combining PI-RADS, SII, %PSA, and the proportion of tumor tissue in biopsy specimens shows excellent predictive performance.

Objective To investigate the synergistic effect of Shugan Huatan Sanjie prescription(SHSF)and paclitaxel on MCF-7/PTX cells of breast cancer and its mechanism.Methods The PTX-resistant breast cancer cell line MCF-7/PTX was established by continuous induction of low concentration.The effects of different concentrations of PTX on MCF-7 and MCF-7/PTX cells.Then the proliferation of MCF-7/PTX cells by SHSF containing serum were detected by MTT assay,and the drug resistance index(RI)and reversion times were calculated according to the IC50 value.MCF-7/PTX cells were divided into blank control group,PTX group(45 nmol·L-1 PTX),SHSF group(7.5％SHSF drug-containing serum)and PTX+SHSF group(45 nmol·L-1 PTX+7.5％SHSF drug-containing serum).The cells were treated with drugs for 24 h.The apoptosis level of each group was detected by flow cytometry.The expression levels of apoptotic proteins Bax,Bcl-2,p-Akt(ser473),Akt,p-mTOR(Ser2448)and mTOR were detected by Western blot.Results The PTX-resistant cell line MCF-7/PTX was established successfully,and the RI value was 6.70.The proliferative activity of MCF-7/PTX cells decreased in a concentration-dependent manner with the increase of SHSF drug-containing serum concentration.And the resistance reversal ratio of 7.5％SHSF serum to MCF-7/PTX cells was 3.48.Compared with blank control group,the apoptosis levels of MCF-7/PTX cells in PTX group and SHSF group were significantly increased(P＜0.01),the protein expression level of Bax was significantly up-regulated(P＜0.01),and the protein expression levels of Bcl-2,p-Akt/Akt and p-mTOR/mTOR were significantly down-regulated(P＜0.05);Compared with PTX group,the apoptosis level in PTX+SHSF group was significantly increased(P＜0.01),the expression level of Bax protein was significantly up-regulated(P＜0.01),and the expression levels of Bcl-2,p-Akt and p-mTOR protein were significantly down-regulated(P＜0.01).Conclusion Shugan Huatan Sanjie Recipe promotes apoptosis by inhibiting Akt/mTOR pathway,and thus plays a synergistic effect with paclitaxel on MCF-7/PTX cells of breast cancer.

This study compared the effects on bone metabolism and morphology of pathological obesity induced by excessive fat intake in a non-hibernator (mice) versus healthy obesity due to pre-hibernation fattening in a hibernator (ground squirrels). Kunming mice were fed a high-fat diet to provide a model of pathological obesity (OB group). Daurian ground squirrels fattened naturally in their pre-hibernation season (PRE group) were used as a healthy obesity model. Micro-computed tomography (micro-CT) and three-point bending tests were used to determine the microstructure and mechanical properties of bone. Western blots were used to analyze protein expression levels related to bone metabolism (Runt-related transcription factor 2 (RunX2), osteocalcin (OCN), alkaline phosphatase (ALP), osteoprotegerin (OPG), receptor activator of nuclear factor-‍κB ligand (RANKL), cathepsin K, matrix metallopeptidase 9 (MMP9), patched protein homolog 1 (Ptch1), phosphorylated β‍-‍catenin (P‍-‍β‍-‍catenin), and glycogen synthase kinase-3β (GSK-3β)). Compared with controls, there was no obvious bone loss in the OB mice, and the stiffness of the femur was increased significantly. Compared with summer active squirrels, bone formation was enhanced but the mechanical properties did not change in the PRE group squirrels. In OB mice, western blots showed significantly increased expression levels of all proteins except RunX2, OPG, and Ptch1. PRE ground squirrels showed significantly increased expression of most proteins except OCN and Ptch1, which decreased significantly, and P‍-‍β‍-‍catenin and OPG, which did not change. In conclusion, for non-hibernating mice, moderate obesity had a certain protective effect on bones, demonstrating two-way regulation, increasing both bone loss and bone formation. For pre-hibernating ground squirrels, the healthy obesity acquired before hibernation had a positive effect on the microstructure of bones, and also enhanced the expression levels of proteins related to bone formation, bone resorption, and Wnt signaling.
Mice ; Animals ; Hibernation ; Core Binding Factor Alpha 1 Subunit/metabolism* ; Glycogen Synthase Kinase 3 beta/metabolism* ; Diet, High-Fat ; X-Ray Microtomography ; Sciuridae/metabolism* ; Obesity

Objective To determine the contents of total flavonoids and xanthohumol in hop from 29 different countries and regions .Methods Rutin colorimetric method was used to determine the content of total flavonoids .HPLC-UV method was established for the determination of xanthohumol in hops .HPLC method was performed by Dikma Technologies Diamonsil C18 (250 mm × 4 .6 mm ,5 μm ) column with mobile phase acetonitrile-1％ glacial acetic acid solution at the flow rate of 1 .0 ml/min .The column temperature was 25 ℃ .The detection wavelength was 370 nm .Results The equation of linear re-gression of total flavonoids was A=30 .345C+0 .0168 ,r=0 .9999 .The equation of linear regression of xanthohumol was A=55446 C+9040 .5 ,r=0 .9999 .Their linear ranges were respectively 20 .2-404 .0 μg/ml ,2 .152-43 .040 μg/ml ,which indica-ted a good linear relationship .The RSDs of precision and repeatability were less than 2％ .The average recoveries of flavonoids and xanthohumol were respectively 102 .71％ and 100 .21％ .Conclusion The contents of total flavonoids and xanthohumol in different hops varieties are significantly different and the import hops was better than the domestic hops in this study .